How to make best use of cross-company data in software effort estimation?

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Citations (Scopus)
248 Downloads (Pure)


Previous works using Cross-Company (CC) data for making Within-Company (WC) Software Eort Estimation (SEE) try to use CC data or models directly to provide directions in the WC context. So, these data or models are only helpful when they match the WC context well. When they do not, a fair amount of WC training data, which are usually expensive to acquire, are still necessary to achieve good performance. We investigate how to make best use of CC data, so that we can reduce the amount of WC data while maintaining or improving performance in comparison to WC SEE models. This is done by proposing a new framework to learn the relationship between CC and WC projects explicitly, allowing CC models to be mapped to the WC context. Such mapped models can be useful even when the CC models themselves do not match the WC context directly. Our study shows that a new approach instantiating this framework is able not only to use substantially less WC data than a corresponding WC model, but also to achieve similar/better performance. This approach can also be used to provide insight into the behaviour of a company in comparison to others.

Original languageEnglish
Title of host publicationICSE '14 : 36th International Conference on Software Engineering Proceedings
PublisherAssociation for Computing Machinery
ISBN (Print)9781450327565
Publication statusPublished - May 2014
EventICSE 2014 : 36th International Conference on Software Engineering - Hyderabad, India
Duration: 31 May 20147 Jun 2014


ConferenceICSE 2014 : 36th International Conference on Software Engineering


  • Software effort estimation
  • cross-company learning
  • transfer learning
  • online learning
  • ensembles of learning machines


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